The “techproducts” example solrconfig.xml has a suggest search component and a /suggest request handler already configured. You can use that as the basis for your configuration, or create it from scratch, as detailed below.

Adding the Suggest Search Component

The first step is to add a search component to solrconfig.xml and tell it to use the SuggestComponent. Here is some sample code that could be used.

Suggester Search Component Parameters

The choice of the lookup implementation (lookupImpl, how terms are found in the suggestion dictionary) and the dictionary implementation (dictionaryImpl, how terms are stored in the suggestion dictionary) will dictate some of the parameters required.

Below are the main parameters that can be used no matter what lookup or dictionary implementation is used. In the following sections additional parameters are provided for each implementation.

searchComponent name

Arbitrary name for the search component.

name

A symbolic name for this suggester. You can refer to this name in the URL parameters and in the SearchHandler configuration. It is possible to have multiples of these in one solrconfig.xml file.

lookupImpl

Lookup implementation. There are several possible implementations, described below in the section Lookup Implementations. If not set, the default lookup is JaspellLookupFactory.

dictionaryImpl

The dictionary implementation to use. There are several possible implementations, described below in the section Dictionary Implementations.

If not set, the default dictionary implementation is HighFrequencyDictionaryFactory. However, if a sourceLocation is used, the dictionary implementation will be FileDictionaryFactory.

field

A field from the index to use as the basis of suggestion terms. If sourceLocation is empty (meaning any dictionary implementation other than FileDictionaryFactory), then terms from this field in the index will be used.

To be used as the basis for a suggestion, the field must be stored. You may want to use copyField rules to create a special 'suggest' field comprised of terms from other fields in documents. In any event, you very likely want a minimal amount of analysis on the field, so an additional option is to create a field type in your schema that only uses basic tokenizers or filters. One option for such a field type is shown here:

However, this minimal analysis is not required if you want more analysis to occur on terms. If using the AnalyzingLookupFactory as your lookupImpl, however, you have the option of defining the field type rules to use for index and query time analysis.

sourceLocation

The path to the dictionary file if using the FileDictionaryFactory. If this value is empty then the main index will be used as a source of terms and weights.

storeDir

The location to store the dictionary file.

buildOnCommit and buildOnOptimize

If true, the lookup data structure will be rebuilt after soft-commit. If false, the default, then the lookup data will be built only when requested by URL parameter suggest.build=true. Use buildOnCommit to rebuild the dictionary with every soft-commit, or buildOnOptimize to build the dictionary only when the index is optimized.

Some lookup implementations may take a long time to build, especially with large indexes. In such cases, using buildOnCommit or buildOnOptimize, particularly with a high frequency of softCommits is not recommended; it’s recommended instead to build the suggester at a lower frequency by manually issuing requests with suggest.build=true.

buildOnStartup

If true, then the lookup data structure will be built when Solr starts or when the core is reloaded. If this parameter is not specified, the suggester will check if the lookup data structure is present on disk and build it if not found.

Enabling this to true could lead to the core talking longer to load (or reload) as the suggester data structure needs to be built, which can sometimes take a long time. It’s usually preferred to have this setting set to false, the default, and build suggesters manually issuing requests with suggest.build=true.

Lookup Implementations

The lookupImpl parameter defines the algorithms used to look up terms in the suggest index. There are several possible implementations to choose from, and some require additional parameters to be configured.

AnalyzingLookupFactory

A lookup that first analyzes the incoming text and adds the analyzed form to a weighted FST, and then does the same thing at lookup time.

This implementation uses the following additional properties:

suggestAnalyzerFieldType

The field type to use for the query-time and build-time term suggestion analysis.

exactMatchFirst

If true, the default, exact suggestions are returned first, even if they are prefixes or other strings in the FST have larger weights.

preserveSep

If true, the default, then a separator between tokens is preserved. This means that suggestions are sensitive to tokenization (e.g., baseball is different from base ball).

preservePositionIncrements

If true, the suggester will preserve position increments. This means that token filters which leave gaps (for example, when StopFilter matches a stopword) the position would be respected when building the suggester. The default is false.

FuzzyLookupFactory

This is a suggester which is an extension of the AnalyzingSuggester but is fuzzy in nature. The similarity is measured by the Levenshtein algorithm.

This implementation uses the following additional properties:

exactMatchFirst

If true, the default, exact suggestions are returned first, even if they are prefixes or other strings in the FST have larger weights.

preserveSep

If true, the default, then a separator between tokens is preserved. This means that suggestions are sensitive to tokenization (e.g., baseball is different from base ball).

maxSurfaceFormsPerAnalyzedForm

The maximum number of surface forms to keep for a single analyzed form. When there are too many surface forms we discard the lowest weighted ones.

maxGraphExpansions

When building the FST ("index-time"), we add each path through the tokenstream graph as an individual entry. This places an upper-bound on how many expansions will be added for a single suggestion. The default is -1 which means there is no limit.

preservePositionIncrements

If true, the suggester will preserve position increments. This means that token filters which leave gaps (for example, when StopFilter matches a stopword) the position would be respected when building the suggester. The default is false.

maxEdits

The maximum number of string edits allowed. The system’s hard limit is 2. The default is 1.

transpositions

If true, the default, transpositions should be treated as a primitive edit operation.

nonFuzzyPrefix

The length of the common non fuzzy prefix match which must match a suggestion. The default is 1.

minFuzzyLength

The minimum length of query before which any string edits will be allowed. The default is 3.

unicodeAware

If true, the maxEdits, minFuzzyLength, transpositions and nonFuzzyPrefix parameters will be measured in unicode code points (actual letters) instead of bytes. The default is false.

AnalyzingInfixLookupFactory

Analyzes the input text and then suggests matches based on prefix matches to any tokens in the indexed text. This uses a Lucene index for its dictionary.

This implementation uses the following additional properties.

indexPath

When using AnalyzingInfixSuggester you can provide your own path where the index will get built. The default is analyzingInfixSuggesterIndexDir and will be created in your collection’s data/ directory.

minPrefixChars

Minimum number of leading characters before PrefixQuery is used (default is 4). Prefixes shorter than this are indexed as character ngrams (increasing index size but making lookups faster).

allTermsRequired

Boolean option for multiple terms. The default is true, all terms will be required.

BlendedInfixLookupFactory

An extension of the AnalyzingInfixSuggester which provides additional functionality to weight prefix matches across the matched documents. You can tell it to score higher if a hit is closer to the start of the suggestion or vice versa.

This implementation uses the following additional properties:

blenderType

Used to calculate weight coefficient using the position of the first matching word. Available options are:

position_linear

weightFieldValue * (1 - 0.10*position): Matches to the start will be given a higher score. This is the default.

position_reciprocal

weightFieldValue / (1 + position): Matches to the end will be given a higher score.

exponent

An optional configuration variable for position_reciprocal to control how fast the score will increase or decrease. Default 2.0.

numFactor

The factor to multiply the number of searched elements from which results will be pruned. Default is 10.

indexPath

When using BlendedInfixSuggester you can provide your own path where the index will get built. The default directory name is blendedInfixSuggesterIndexDir and will be created in your collection’s data directory.

minPrefixChars

Minimum number of leading characters before PrefixQuery is used (the default is 4). Prefixes shorter than this are indexed as character ngrams, which increases index size but makes lookups faster.

FreeTextLookupFactory

It looks at the last tokens plus the prefix of whatever final token the user is typing, if present, to predict the most likely next token. The number of previous tokens that need to be considered can also be specified. This suggester would only be used as a fallback, when the primary suggester fails to find any suggestions.

This implementation uses the following additional properties:

suggestFreeTextAnalyzerFieldType

The analyzer used at "query-time" and "build-time" to analyze suggestions. This parameter is required.

ngrams

The max number of tokens out of which singles will be made the dictionary. The default value is 2. Increasing this would mean you want more than the previous 2 tokens to be taken into consideration when making the suggestions.

FSTLookupFactory

An automaton-based lookup. This implementation is slower to build, but provides the lowest memory cost. We recommend using this implementation unless you need more sophisticated matching results, in which case you should use the Jaspell implementation.

This implementation uses the following additional properties:

exactMatchFirst

If true, the default, exact suggestions are returned first, even if they are prefixes or other strings in the FST have larger weights.

weightBuckets

The number of separate buckets for weights which the suggester will use while building its dictionary.

TSTLookupFactory

A simple compact ternary trie based lookup.

WFSTLookupFactory

A weighted automaton representation which is an alternative to FSTLookup for more fine-grained ranking. WFSTLookup does not use buckets, but instead a shortest path algorithm.

Note that it expects weights to be whole numbers. If weight is missing it’s assumed to be 1.0. Weights affect the sorting of matching suggestions when spellcheck.onlyMorePopular=true is selected: weights are treated as "popularity" score, with higher weights preferred over suggestions with lower weights.

JaspellLookupFactory

A more complex lookup based on a ternary trie from the JaSpell project. Use this implementation if you need more sophisticated matching results.

Dictionary Implementations

The dictionary implementations define how terms are stored. There are several options, and multiple dictionaries can be used in a single request if necessary.

DocumentDictionaryFactory

A dictionary with terms, weights, and an optional payload taken from the index.

This dictionary implementation takes the following parameters in addition to parameters described for the Suggester generally and for the lookup implementation:

weightField

A field that is stored or a numeric DocValue field. This parameter is optional.

payloadField

The payloadField should be a field that is stored. This parameter is optional.

contextField

Field to be used for context filtering. Note that only some lookup implementations support filtering.

DocumentExpressionDictionaryFactory

This dictionary implementation is the same as the DocumentDictionaryFactory but allows users to specify an arbitrary expression into the weightExpression tag.

This dictionary implementation takes the following parameters in addition to parameters described for the Suggester generally and for the lookup implementation:

payloadField

The payloadField should be a field that is stored. This parameter is optional.

weightExpression

An arbitrary expression used for scoring the suggestions. The fields used must be numeric fields. This parameter is required.

contextField

Field to be used for context filtering. Note that only some lookup implementations support filtering.

HighFrequencyDictionaryFactory

This dictionary implementation allows adding a threshold to prune out less frequent terms in cases where very common terms may overwhelm other terms.

This dictionary implementation takes one parameter in addition to parameters described for the Suggester generally and for the lookup implementation:

threshold

A value between zero and one representing the minimum fraction of the total documents where a term should appear in order to be added to the lookup dictionary.

FileDictionaryFactory

This dictionary implementation allows using an external file that contains suggest entries. Weights and payloads can also be used.

If using a dictionary file, it should be a plain text file in UTF-8 encoding. You can use both single terms and phrases in the dictionary file. If adding weights or payloads, those should be separated from terms using the delimiter defined with the fieldDelimiter property (the default is '\t', the tab representation). If using payloads, the first line in the file must specify a payload.

This dictionary implementation takes one parameter in addition to parameters described for the Suggester generally and for the lookup implementation:

fieldDelimiter

Specifies the delimiter to be used separating the entries, weights and payloads. The default is tab (\t).

Example File

acquire
accidentally 2.0
accommodate 3.0

Multiple Dictionaries

It is possible to include multiple dictionaryImpl definitions in a single SuggestComponent definition.

When using these Suggesters in a query, you would define multiple suggest.dictionary parameters in the request, referring to the names given for each Suggester in the search component definition. The response will include the terms in sections for each Suggester. See the Example Usages section below for an example request and response.

Adding the Suggest Request Handler

After adding the search component, a request handler must be added to solrconfig.xml. This request handler works the same as any other request handler, and allows you to configure default parameters for serving suggestion requests. The request handler definition must incorporate the "suggest" search component defined previously.

Suggest Request Handler Parameters

The following parameters allow you to set defaults for the Suggest request handler:

suggest=true

This parameter should always be true, because we always want to run the Suggester for queries submitted to this handler.

suggest.dictionary

The name of the dictionary component configured in the search component. This is a mandatory parameter. It can be set in the request handler, or sent as a parameter at query time.

suggest.q

The query to use for suggestion lookups.

suggest.count

Specifies the number of suggestions for Solr to return.

suggest.cfq

A Context Filter Query used to filter suggestions based on the context field, if supported by the suggester.

suggest.build

If true, it will build the suggester index. This is likely useful only for initial requests; you would probably not want to build the dictionary on every request, particularly in a production system. If you would like to keep your dictionary up to date, you should use the buildOnCommit or buildOnOptimize parameter for the search component.

suggest.reload

If true, it will reload the suggester index.

suggest.buildAll

If true, it will build all suggester indexes.

suggest.reloadAll

If true, it will reload all suggester indexes.

These properties can also be overridden at query time, or not set in the request handler at all and always sent at query time.

Context Filtering

Context filtering (suggest.cfq) is currently only supported by AnalyzingInfixLookupFactory and BlendedInfixLookupFactory, and only when backed by a Document*Dictionary. All other implementations will return unfiltered matches as if filtering was not requested.

Example Usages

Get Suggestions with Weights

This is a basic suggestion using a single dictionary and a single Solr core.

In this example, we’ve simply requested the string 'elec' with the suggest.q parameter and requested that the suggestion dictionary be built with suggest.build (note, however, that you would likely not want to build the index on every query - instead you should use buildOnCommit or buildOnOptimize if you have regularly changing documents).

Context Filtering

Context filtering lets you filter suggestions by a separate context field, such as category, department or any other token. The AnalyzingInfixLookupFactory and BlendedInfixLookupFactory currently support this feature, when backed by DocumentDictionaryFactory.

Add contextField to your suggester configuration. This example will suggest names and allow to filter by category: